Dynamics of offshore structures, ships, and underwater vehicles such as manned submersibles are complicated and highly nonlinear to be considered with conventional dynamic theories, especially when they are operated in definitely slow speed. Moreover, the dynamics may be changed during operation. In order to deal with such a complex and time varying dynamics, the neural network I/O system is advantageous taking advantage of learning ability even if the input and the output are multiple. In this research, the controller and the identification model consist of the artificial neural network, and the controller is modified adaptively based on the I/O relation of the identification model.This year, a structure of feed forward neural network and its learning process were proposed to simulate the dynamic behavior of the controlled object. The network includes two kinds of recurrent connections, i.e., from the output layr to the input layr and from the hidden layr to the input layr. The first connection enables the network to obtain the input state variables from its own outputs and the second one is to keep the influence of the past data in itself. In this paper, the learning process is improved to equip the network with the capability of emulating the dynamic behavior including higher-order finite differences. The proposed network is adopted to the neural-network-based control system called "SONCS : Self-Organizing Neural-net Controller System" , which has been developed as an adaptive control system for Underwater Robots. The neural network controller in SONCS can be quickly adapted taking advantage of the network's simulating ability. The efficiency of the network is successfully demonstrated through the application to heading keeping control of a versatile robot called "Twin-Burger".実ロボットを使った実験において、構築したシステムは良好なコントローラを実時間で作り出すことに成功しており、提案したシステムの有用性を示した。